scholarly journals Accuracy-Energy Configurable Sensor Processor and IoT Device for Long-Term Activity Monitoring in Rare-Event Sensing Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Daejin Park ◽  
Jeonghun Cho
Keyword(s):  
Rare Event ◽  
Sensor Data ◽  
Event Generator ◽  
Signal Acquisition ◽  
Processing Unit ◽  
Sensor Signal ◽  
Single Chip ◽  
Sensing Applications ◽  
Tightly Coupled ◽  
Sensor Processor

A specially designed sensor processor used as a main processor in IoT (internet-of-thing) device for the rare-event sensing applications is proposed. The IoT device including the proposed sensor processor performs the event-driven sensor data processing based on an accuracy-energy configurable event-quantization in architectural level. The received sensor signal is converted into a sequence of atomic events, which is extracted by the signal-to-atomic-event generator (AEG). Using an event signal processing unit (EPU) as an accelerator, the extracted atomic events are analyzed to build the final event. Instead of the sampled raw data transmission via internet, the proposed method delays the communication with a host system until a semantic pattern of the signal is identified as a final event. The proposed processor is implemented on a single chip, which is tightly coupled in bus connection level with a microcontroller using a 0.18 μm CMOS embedded-flash process. For experimental results, we evaluated the proposed sensor processor by using an IR- (infrared radio-) based signal reflection and sensor signal acquisition system. We successfully demonstrated that the expected power consumption is in the range of 20% to 50% compared to the result of the basement in case of allowing 10% accuracy error.

scholarly journals A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Daejin Park ◽  
Jonghee M. Youn ◽  
Jeonghun Cho
Keyword(s):  
Signal Processing ◽  
Data Processing ◽  
Energy Efficient ◽  
Rare Event ◽  
Sensor Data ◽  
Processing Unit ◽  
Sensing Applications ◽  
Event Driven ◽  
Iot Devices ◽  
Activity Sensing

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.

Bayesian networks based rare event prediction with sensor data

2009 ◽  
Vol 22 (5) ◽  
pp. 336-343 ◽  
Author(s):  
Seong-Pyo Cheon ◽  
Sungshin Kim ◽  
So-Young Lee ◽  
Chong-Bum Lee
Keyword(s):  
Bayesian Networks ◽  
Rare Event ◽  
Sensor Data ◽  
Event Prediction

Study of the Metal Crack Propagation by Acoustic Emission Testing

2011 ◽  
Vol 105-107 ◽  
pp. 2179-2182
Author(s):  
Wei Min Zhang ◽  
Shu Xuan Liu ◽  
Yong Qiu ◽  
Cheng Feng Chen
Keyword(s):  
Acoustic Emission ◽  
Crack Propagation ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Sensor Signal ◽  
Acoustic Emission Sensor ◽  
Axial Tension ◽  
Emission Testing ◽  
Acoustic Emission Testing ◽  
Signal Operating

Crack propagation is the main reason which leads to the invalidity of the metal components. A set of detecting equipment based on the acoustic emission method was designed, and it was mainly composed of acoustic emission sensor, signal operating circuits and signal acquisition system. Specimens of 16MnR material were manufactured and the static axial tension test of them was carried on. Acoustic emission signals from the specimen were detected by acoustic emission equipment by using piezoelectric ceramic sensor. Signal datum were acquired and operated by the acquisition system, as well as the acquisition program written for it. The final results has demonstrated that acoustic emission equipment designed for the test performed well in acquiring the signals induced by the metal crack propagation.

scholarly journals Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 846
Author(s):  
Liang Zhao ◽  
Yu Bao ◽  
Yu Zhang ◽  
Ruidong Ye ◽  
Aijuan Zhang
Keyword(s):  
Chest Compression ◽  
Sensor Data ◽  
Evaluation Procedure ◽  
Support Vector ◽  
Portable Devices ◽  
Sensor Signal ◽  
Distance Measurements ◽  
Compression Depth ◽  
One Dimensional ◽  
Starting Point

When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

Sensor Fusion Based on Strong Tracking Filter for Augmented Reality Registration

2011 ◽  
Vol 467-469 ◽  
pp. 108-113
Author(s):  
Xin Yu Li ◽  
Dong Yi Chen
Keyword(s):  
Augmented Reality ◽  
Inertial Sensor ◽  
Measurement Data ◽  
Sensor Data ◽  
Data Synchronization ◽  
Error Covariance ◽  
Tightly Coupled ◽  
Strong Tracking Filter ◽  
Fading Factor ◽  
Tracking Filter

Accurate tracking for Augmented Reality applications is a challenging task. Multi-sensors hybrid tracking generally provide more stable than the effect of the single visual tracking. This paper presents a new tightly-coupled hybrid tracking approach combining vision-based systems with inertial sensor. Based on multi-frequency sampling theory in the measurement data synchronization, a strong tracking filter (STF) is used to smooth sensor data and estimate position and orientation. Through adding time-varying fading factor to adaptively adjust the prediction error covariance of filter, this method improves the performance of tracking for fast moving targets. Experimental results show the efficiency and robustness of this proposed approach.

The Achievement of Intelligent Lubrication Adjustment Function of CNC Machine Tools Based on SSAS

2014 ◽  
Vol 556-562 ◽  
pp. 1454-1459
Author(s):  
Dong Sheng You
Keyword(s):  
Machine Tools ◽  
Sensor Data ◽  
Lubricating Oil ◽  
Cnc Machine Tools ◽  
Signal Acquisition ◽  
Cnc Machine ◽  
Lubrication Mode ◽  
Free Function ◽  
And Control ◽  
The Relationship

The use of CNC machine tools signal acquisition, two-way transmission of the temperature sensor data, the ladder design and macro program guide and other methods on the implementation of a temperature sensing system of smart lubrication function. It is not only low-end CNC machine tools can compensate for deficiencies in equipment protection features and maintenance-free function, but also enhance the diversity of processing. Ultimately by analyzing the different lubrication mode, the relationship between the lubricating oil pressure and temperature and other factors, to draw the function in the lubrication in a stabilizing effect on oil pressure and control bearings and nut seat temperature. It is simple and practical, has important theoretical significance and great value.

The Design of the Solar Panels Automatic Tracking Controller

2014 ◽  
Vol 953-954 ◽  
pp. 123-127
Author(s):  
Rong Xia Sun ◽  
Xiao Ning Sun ◽  
Shuo Nan Wang
Keyword(s):  
Control Device ◽  
Light Signal ◽  
Control Circuit ◽  
Signal Acquisition ◽  
Single Chip ◽  
Level Control ◽  
Physical Test ◽  
Automatic Tracking ◽  
Tracking Controller ◽  
Driving Circuit

In this paper, the design is with the single chip microcomputer as the core of automatic tracking controller. The system is mainly composed of the signal acquisition part, the signal conditioning part, a control circuit and a drive circuit. The signal collection circuit composed of photosensitive resistance sensors to collect light signal, signal conversion circuit with voltage follower LM324 convert the change of light intensity to the change of the voltage, through the voltage comparator LM393 produce high and low level control stepping motor rotation; Control circuit use the AT89S52 as the main control device, output different control signals to the driving circuit; Driving circuit use the ULN2003 as driver stepper motor. Is obtained by simulation debugging, physical test, the error rate is less than 5%, in order to realize the efficient utilization of solar energy.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

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